Overview
Brought to you by YData
Dataset statistics
| Number of variables | 14 |
|---|---|
| Number of observations | 4440 |
| Missing cells | 3990 |
| Missing cells (%) | 6.4% |
| Duplicate rows | 0 |
| Duplicate rows (%) | 0.0% |
| Total size in memory | 8.6 MiB |
| Average record size in memory | 2.0 KiB |
Variable types
| Text | 9 |
|---|---|
| Numeric | 2 |
| Categorical | 3 |
Cited by is highly overall correlated with Year | High correlation |
Document Type is highly overall correlated with Open Access | High correlation |
Open Access is highly overall correlated with Document Type | High correlation |
Year is highly overall correlated with Cited by | High correlation |
Language of Original Document is highly imbalanced (93.8%) | Imbalance |
Document Type is highly imbalanced (68.0%) | Imbalance |
Author full names has 97 (2.2%) missing values | Missing |
Author(s) ID has 97 (2.2%) missing values | Missing |
Affiliations has 107 (2.4%) missing values | Missing |
Authors with affiliations has 107 (2.4%) missing values | Missing |
Author Keywords has 1042 (23.5%) missing values | Missing |
Open Access has 2528 (56.9%) missing values | Missing |
Cited by is highly skewed (γ1 = 44.55171865) | Skewed |
EID has unique values | Unique |
Cited by has 1620 (36.5%) zeros | Zeros |
Reproduction
| Analysis started | 2024-11-01 19:27:20.956753 |
|---|---|
| Analysis finished | 2024-11-01 19:27:25.270592 |
| Duration | 4.31 seconds |
| Software version | ydata-profiling vv4.12.0 |
| Download configuration | config.json |
Variables
Missing
| Distinct | 4070 |
|---|---|
| Distinct (%) | 93.7% |
| Missing | 97 |
| Missing (%) | 2.2% |
| Memory size | 811.2 KiB |
Length
| Max length | 928 |
|---|---|
| Median length | 297 |
| Mean length | 120.31798 |
| Min length | 21 |
Unique
| Unique | 3856 ? |
|---|---|
| Unique (%) | 88.8% |
Sample
| 1st row | Puri, Rishabh (59230247300); Onishi, Junya (7003942439); Rüttgers, Mario (57205467321); Sarma, Rakesh (56448274800); Tsubokura, Makoto (57543345100); Lintermann, Andreas (36573883800) |
|---|---|
| 2nd row | Banerjee, Srutarshi (57212204932); Rodrigues, Miesher (24483782300); Ballester, Manuel (57336856100); Vija, Alexander H. (8521136100); Katsaggelos, Aggelos K. (7102711302) |
| 3rd row | Agraz, Melih (57188574966) |
| 4th row | Cen, Jianhuan (58172058200); Zou, Qingsong (8369763900) |
| 5th row | Yao, Liaojun (54584768400); Wang, Jiexiong (57776191800); Chuai, Mingyue (58485210500); Lomov, Stepan V. (7005067917); Carvelli, V. (6603539304) |
| Value | Count | Frequency (%) |
| wang | 711 | 1.3% |
| zhang | 589 | 1.0% |
| li | 558 | 1.0% |
| liu | 456 | 0.8% |
| chen | 400 | 0.7% |
| yang | 289 | 0.5% |
| a | 266 | 0.5% |
| m | 231 | 0.4% |
| xu | 207 | 0.4% |
| wu | 198 | 0.3% |
| Other values (25216) | 52870 |
Most occurring characters
| Value | Count | Frequency (%) |
| 52413 | 10.0% | |
| 0 | 37703 | 7.2% |
| 5 | 27599 | 5.3% |
| a | 24348 | 4.7% |
| 7 | 20822 | 4.0% |
| n | 20003 | 3.8% |
| 2 | 19252 | 3.7% |
| i | 18273 | 3.5% |
| ( | 17910 | 3.4% |
| ) | 17910 | 3.4% |
| Other values (105) | 266308 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 522541 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 52413 | 10.0% | |
| 0 | 37703 | 7.2% |
| 5 | 27599 | 5.3% |
| a | 24348 | 4.7% |
| 7 | 20822 | 4.0% |
| n | 20003 | 3.8% |
| 2 | 19252 | 3.7% |
| i | 18273 | 3.5% |
| ( | 17910 | 3.4% |
| ) | 17910 | 3.4% |
| Other values (105) | 266308 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 522541 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 52413 | 10.0% | |
| 0 | 37703 | 7.2% |
| 5 | 27599 | 5.3% |
| a | 24348 | 4.7% |
| 7 | 20822 | 4.0% |
| n | 20003 | 3.8% |
| 2 | 19252 | 3.7% |
| i | 18273 | 3.5% |
| ( | 17910 | 3.4% |
| ) | 17910 | 3.4% |
| Other values (105) | 266308 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 522541 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 52413 | 10.0% | |
| 0 | 37703 | 7.2% |
| 5 | 27599 | 5.3% |
| a | 24348 | 4.7% |
| 7 | 20822 | 4.0% |
| n | 20003 | 3.8% |
| 2 | 19252 | 3.7% |
| i | 18273 | 3.5% |
| ( | 17910 | 3.4% |
| ) | 17910 | 3.4% |
| Other values (105) | 266308 |
Author(s) ID
Text
Missing
| Distinct | 4006 |
|---|---|
| Distinct (%) | 92.2% |
| Missing | 97 |
| Missing (%) | 2.2% |
| Memory size | 461.5 KiB |
Length
| Max length | 395 |
|---|---|
| Median length | 252 |
| Mean length | 51.061248 |
| Min length | 10 |
Unique
| Unique | 3751 ? |
|---|---|
| Unique (%) | 86.4% |
Sample
| 1st row | 59230247300; 7003942439; 57205467321; 56448274800; 57543345100; 36573883800 |
|---|---|
| 2nd row | 57212204932; 24483782300; 57336856100; 8521136100; 7102711302 |
| 3rd row | 57188574966 |
| 4th row | 58172058200; 8369763900 |
| 5th row | 54584768400; 57776191800; 58485210500; 7005067917; 6603539304 |
| Value | Count | Frequency (%) |
| 55665002100 | 76 | 0.4% |
| 7003655679 | 36 | 0.2% |
| 8453033200 | 23 | 0.1% |
| 57370095000 | 23 | 0.1% |
| 12645288600 | 21 | 0.1% |
| 57034330100 | 18 | 0.1% |
| 55870784900 | 18 | 0.1% |
| 35194560500 | 17 | 0.1% |
| 57192679570 | 16 | 0.1% |
| 56145252100 | 16 | 0.1% |
| Other values (12511) | 17642 |
Most occurring characters
| Value | Count | Frequency (%) |
| 0 | 37702 | |
| 5 | 27599 | |
| 7 | 20822 | |
| 2 | 19252 | |
| 1 | 16551 | |
| 6 | 15978 | |
| 9 | 14501 | 6.5% |
| 3 | 14405 | 6.5% |
| 8 | 14333 | 6.5% |
| ; | 13563 | 6.1% |
| Other values (2) | 27053 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 221759 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 0 | 37702 | |
| 5 | 27599 | |
| 7 | 20822 | |
| 2 | 19252 | |
| 1 | 16551 | |
| 6 | 15978 | |
| 9 | 14501 | 6.5% |
| 3 | 14405 | 6.5% |
| 8 | 14333 | 6.5% |
| ; | 13563 | 6.1% |
| Other values (2) | 27053 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 221759 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 0 | 37702 | |
| 5 | 27599 | |
| 7 | 20822 | |
| 2 | 19252 | |
| 1 | 16551 | |
| 6 | 15978 | |
| 9 | 14501 | 6.5% |
| 3 | 14405 | 6.5% |
| 8 | 14333 | 6.5% |
| ; | 13563 | 6.1% |
| Other values (2) | 27053 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 221759 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 0 | 37702 | |
| 5 | 27599 | |
| 7 | 20822 | |
| 2 | 19252 | |
| 1 | 16551 | |
| 6 | 15978 | |
| 9 | 14501 | 6.5% |
| 3 | 14405 | 6.5% |
| 8 | 14333 | 6.5% |
| ; | 13563 | 6.1% |
| Other values (2) | 27053 |
Title
Text
| Distinct | 4394 |
|---|---|
| Distinct (%) | 99.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 711.7 KiB |
Length
| Max length | 547 |
|---|---|
| Median length | 177 |
| Mean length | 99.953153 |
| Min length | 15 |
Unique
| Unique | 4377 ? |
|---|---|
| Unique (%) | 98.6% |
Sample
| 1st row | On the choice of physical constraints in artificial neural networks for predicting flow fields |
|---|---|
| 2nd row | A physics based machine learning model to characterize room temperature semiconductor detectors in 3D |
| 3rd row | Evaluating single multiplicative neuron models in physics-informed neural networks for differential equations |
| 4th row | Deep finite volume method for partial differential equations |
| 5th row | Physics-informed machine learning for loading history dependent fatigue delamination of composite laminates |
| Value | Count | Frequency (%) |
| for | 2335 | 4.3% |
| neural | 2236 | 4.1% |
| physics-informed | 2111 | 3.9% |
| of | 1905 | 3.5% |
| networks | 1356 | 2.5% |
| learning | 1348 | 2.5% |
| and | 1213 | 2.2% |
| a | 1108 | 2.0% |
| network | 888 | 1.6% |
| in | 886 | 1.6% |
| Other values (7047) | 38681 |
Most occurring characters
| Value | Count | Frequency (%) |
| 49468 | 11.1% | |
| e | 37136 | 8.4% |
| i | 32703 | 7.4% |
| n | 30554 | 6.9% |
| o | 27896 | 6.3% |
| r | 27220 | 6.1% |
| a | 25714 | 5.8% |
| t | 23072 | 5.2% |
| s | 22078 | 5.0% |
| l | 15215 | 3.4% |
| Other values (227) | 152736 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 443792 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 49468 | 11.1% | |
| e | 37136 | 8.4% |
| i | 32703 | 7.4% |
| n | 30554 | 6.9% |
| o | 27896 | 6.3% |
| r | 27220 | 6.1% |
| a | 25714 | 5.8% |
| t | 23072 | 5.2% |
| s | 22078 | 5.0% |
| l | 15215 | 3.4% |
| Other values (227) | 152736 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 443792 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 49468 | 11.1% | |
| e | 37136 | 8.4% |
| i | 32703 | 7.4% |
| n | 30554 | 6.9% |
| o | 27896 | 6.3% |
| r | 27220 | 6.1% |
| a | 25714 | 5.8% |
| t | 23072 | 5.2% |
| s | 22078 | 5.0% |
| l | 15215 | 3.4% |
| Other values (227) | 152736 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 443792 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 49468 | 11.1% | |
| e | 37136 | 8.4% |
| i | 32703 | 7.4% |
| n | 30554 | 6.9% |
| o | 27896 | 6.3% |
| r | 27220 | 6.1% |
| a | 25714 | 5.8% |
| t | 23072 | 5.2% |
| s | 22078 | 5.0% |
| l | 15215 | 3.4% |
| Other values (227) | 152736 |
Year
Real number (ℝ)
High correlation
| Distinct | 10 |
|---|---|
| Distinct (%) | 0.2% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 2022.7995 |
| Minimum | 2016 |
|---|---|
| Maximum | 2025 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 34.8 KiB |
Quantile statistics
| Minimum | 2016 |
|---|---|
| 5-th percentile | 2020 |
| Q1 | 2022 |
| median | 2023 |
| Q3 | 2024 |
| 95-th percentile | 2024 |
| Maximum | 2025 |
| Range | 9 |
| Interquartile range (IQR) | 2 |
Descriptive statistics
| Standard deviation | 1.2890094 |
|---|---|
| Coefficient of variation (CV) | 0.0006372403 |
| Kurtosis | 2.7706132 |
| Mean | 2022.7995 |
| Median Absolute Deviation (MAD) | 1 |
| Skewness | -1.3803014 |
| Sum | 8981230 |
| Variance | 1.6615452 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=10)
| Value | Count | Frequency (%) |
| 2024 | 1603 | |
| 2023 | 1348 | |
| 2022 | 841 | |
| 2021 | 400 | 9.0% |
| 2020 | 150 | 3.4% |
| 2019 | 51 | 1.1% |
| 2017 | 17 | 0.4% |
| 2018 | 12 | 0.3% |
| 2025 | 10 | 0.2% |
| 2016 | 8 | 0.2% |
| Value | Count | Frequency (%) |
| 2016 | 8 | 0.2% |
| 2017 | 17 | 0.4% |
| 2018 | 12 | 0.3% |
| 2019 | 51 | 1.1% |
| 2020 | 150 | 3.4% |
| 2021 | 400 | 9.0% |
| 2022 | 841 | |
| 2023 | 1348 | |
| 2024 | 1603 | |
| 2025 | 10 | 0.2% |
| Value | Count | Frequency (%) |
| 2025 | 10 | 0.2% |
| 2024 | 1603 | |
| 2023 | 1348 | |
| 2022 | 841 | |
| 2021 | 400 | 9.0% |
| 2020 | 150 | 3.4% |
| 2019 | 51 | 1.1% |
| 2018 | 12 | 0.3% |
| 2017 | 17 | 0.4% |
| 2016 | 8 | 0.2% |
Source title
Text
| Distinct | 1467 |
|---|---|
| Distinct (%) | 33.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 436.6 KiB |
Length
| Max length | 267 |
|---|---|
| Median length | 131 |
| Mean length | 43.667793 |
| Min length | 4 |
Unique
| Unique | 833 ? |
|---|---|
| Unique (%) | 18.8% |
Sample
| 1st row | Future Generation Computer Systems |
|---|---|
| 2nd row | Scientific Reports |
| 3rd row | Scientific Reports |
| 4th row | Journal of Computational Physics |
| 5th row | Composites Part A: Applied Science and Manufacturing |
| Value | Count | Frequency (%) |
| and | 1624 | 6.5% |
| of | 1444 | 5.8% |
| journal | 789 | 3.2% |
| in | 784 | 3.1% |
| engineering | 773 | 3.1% |
| conference | 623 | 2.5% |
| international | 621 | 2.5% |
| on | 598 | 2.4% |
| proceedings | 559 | 2.2% |
| ieee | 439 | 1.8% |
| Other values (1608) | 16701 |
Most occurring characters
| Value | Count | Frequency (%) |
| 20521 | 10.6% | |
| n | 17994 | 9.3% |
| e | 17272 | 8.9% |
| i | 14117 | 7.3% |
| o | 12212 | 6.3% |
| a | 11227 | 5.8% |
| t | 9967 | 5.1% |
| r | 9389 | 4.8% |
| c | 8628 | 4.5% |
| s | 8189 | 4.2% |
| Other values (64) | 64369 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 193885 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 20521 | 10.6% | |
| n | 17994 | 9.3% |
| e | 17272 | 8.9% |
| i | 14117 | 7.3% |
| o | 12212 | 6.3% |
| a | 11227 | 5.8% |
| t | 9967 | 5.1% |
| r | 9389 | 4.8% |
| c | 8628 | 4.5% |
| s | 8189 | 4.2% |
| Other values (64) | 64369 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 193885 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 20521 | 10.6% | |
| n | 17994 | 9.3% |
| e | 17272 | 8.9% |
| i | 14117 | 7.3% |
| o | 12212 | 6.3% |
| a | 11227 | 5.8% |
| t | 9967 | 5.1% |
| r | 9389 | 4.8% |
| c | 8628 | 4.5% |
| s | 8189 | 4.2% |
| Other values (64) | 64369 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 193885 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 20521 | 10.6% | |
| n | 17994 | 9.3% |
| e | 17272 | 8.9% |
| i | 14117 | 7.3% |
| o | 12212 | 6.3% |
| a | 11227 | 5.8% |
| t | 9967 | 5.1% |
| r | 9389 | 4.8% |
| c | 8628 | 4.5% |
| s | 8189 | 4.2% |
| Other values (64) | 64369 |
Cited by
Real number (ℝ)
High correlation Skewed Zeros
| Distinct | 182 |
|---|---|
| Distinct (%) | 4.1% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 14.777027 |
| Minimum | 0 |
|---|---|
| Maximum | 6648 |
| Zeros | 1620 |
| Zeros (%) | 36.5% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 34.8 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 0 |
| Q1 | 0 |
| median | 2 |
| Q3 | 8 |
| 95-th percentile | 51.05 |
| Maximum | 6648 |
| Range | 6648 |
| Interquartile range (IQR) | 8 |
Descriptive statistics
| Standard deviation | 116.71375 |
|---|---|
| Coefficient of variation (CV) | 7.898324 |
| Kurtosis | 2411.3149 |
| Mean | 14.777027 |
| Median Absolute Deviation (MAD) | 2 |
| Skewness | 44.551719 |
| Sum | 65610 |
| Variance | 13622.099 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 0 | 1620 | |
| 1 | 531 | 12.0% |
| 2 | 323 | 7.3% |
| 3 | 233 | 5.2% |
| 4 | 217 | 4.9% |
| 5 | 132 | 3.0% |
| 6 | 129 | 2.9% |
| 7 | 108 | 2.4% |
| 9 | 84 | 1.9% |
| 10 | 83 | 1.9% |
| Other values (172) | 980 |
| Value | Count | Frequency (%) |
| 0 | 1620 | |
| 1 | 531 | 12.0% |
| 2 | 323 | 7.3% |
| 3 | 233 | 5.2% |
| 4 | 217 | 4.9% |
| 5 | 132 | 3.0% |
| 6 | 129 | 2.9% |
| 7 | 108 | 2.4% |
| 8 | 77 | 1.7% |
| 9 | 84 | 1.9% |
| Value | Count | Frequency (%) |
| 6648 | 1 | |
| 2632 | 1 | |
| 1082 | 1 | |
| 942 | 1 | |
| 633 | 1 | |
| 563 | 1 | |
| 543 | 1 | |
| 542 | 1 | |
| 525 | 1 | |
| 511 | 1 |
Affiliations
Text
Missing
| Distinct | 4077 |
|---|---|
| Distinct (%) | 94.1% |
| Missing | 107 |
| Missing (%) | 2.4% |
| Memory size | 1.3 MiB |
Length
| Max length | 2210 |
|---|---|
| Median length | 570 |
| Mean length | 231.86407 |
| Min length | 3 |
Unique
| Unique | 3894 ? |
|---|---|
| Unique (%) | 89.9% |
Sample
| 1st row | Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich, 52425, Germany; RIKEN Center for Computational Science, 7-1-26, Minatojima-minami-machi, Chuo-ku, Hyogo, Kobe, 650-0047, Japan; Engler-Bunte Institute, Combustion Technology, Karlsruhe Institute for Technology, Engler-Bunte Ring 7, Karlsruhe, 76131, Germany |
|---|---|
| 2nd row | Northwestern University, 2145 Sheridan Road, Evanston, 60208, IL, United States; Siemens Medical Solutions USA, Inc., Hoffman Estates, 60192, IL, United States |
| 3rd row | Department of Statistics, Giresun University, Giresun, 28200, Turkey |
| 4th row | School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China; School of Computer Science and Engineering, Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, 510006, China |
| 5th row | Department of Astronautics Science and Mechanics, Harbin Institute of Technology, Harbin, China; Department of Materials Engineering, KU Leuven, Belgium; Department A.B.C., Politecnico di Milano, Italy |
| Value | Count | Frequency (%) |
| of | 11585 | 9.1% |
| university | 6786 | 5.3% |
| and | 4928 | 3.9% |
| engineering | 4144 | 3.3% |
| united | 3302 | 2.6% |
| department | 3280 | 2.6% |
| china | 3274 | 2.6% |
| states | 2945 | 2.3% |
| technology | 1979 | 1.6% |
| science | 1821 | 1.4% |
| Other values (9532) | 83068 |
Most occurring characters
| Value | Count | Frequency (%) |
| 122744 | 12.2% | |
| e | 80139 | 8.0% |
| n | 78018 | 7.8% |
| i | 71068 | 7.1% |
| a | 62612 | 6.2% |
| t | 56537 | 5.6% |
| o | 50026 | 5.0% |
| r | 42808 | 4.3% |
| , | 41458 | 4.1% |
| s | 30375 | 3.0% |
| Other values (122) | 368882 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 1004667 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 122744 | 12.2% | |
| e | 80139 | 8.0% |
| n | 78018 | 7.8% |
| i | 71068 | 7.1% |
| a | 62612 | 6.2% |
| t | 56537 | 5.6% |
| o | 50026 | 5.0% |
| r | 42808 | 4.3% |
| , | 41458 | 4.1% |
| s | 30375 | 3.0% |
| Other values (122) | 368882 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 1004667 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 122744 | 12.2% | |
| e | 80139 | 8.0% |
| n | 78018 | 7.8% |
| i | 71068 | 7.1% |
| a | 62612 | 6.2% |
| t | 56537 | 5.6% |
| o | 50026 | 5.0% |
| r | 42808 | 4.3% |
| , | 41458 | 4.1% |
| s | 30375 | 3.0% |
| Other values (122) | 368882 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 1004667 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 122744 | 12.2% | |
| e | 80139 | 8.0% |
| n | 78018 | 7.8% |
| i | 71068 | 7.1% |
| a | 62612 | 6.2% |
| t | 56537 | 5.6% |
| o | 50026 | 5.0% |
| r | 42808 | 4.3% |
| , | 41458 | 4.1% |
| s | 30375 | 3.0% |
| Other values (122) | 368882 |
Missing
| Distinct | 4249 |
|---|---|
| Distinct (%) | 98.1% |
| Missing | 107 |
| Missing (%) | 2.4% |
| Memory size | 2.8 MiB |
Length
| Max length | 3824 |
|---|---|
| Median length | 1071 |
| Mean length | 534.994 |
| Min length | 15 |
Unique
| Unique | 4178 ? |
|---|---|
| Unique (%) | 96.4% |
Sample
| 1st row | Puri R., Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich, 52425, Germany, Engler-Bunte Institute, Combustion Technology, Karlsruhe Institute for Technology, Engler-Bunte Ring 7, Karlsruhe, 76131, Germany; Onishi J., RIKEN Center for Computational Science, 7-1-26, Minatojima-minami-machi, Chuo-ku, Hyogo, Kobe, 650-0047, Japan; Rüttgers M., Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich, 52425, Germany; Sarma R., Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich, 52425, Germany; Tsubokura M., RIKEN Center for Computational Science, 7-1-26, Minatojima-minami-machi, Chuo-ku, Hyogo, Kobe, 650-0047, Japan; Lintermann A., Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich, 52425, Germany |
|---|---|
| 2nd row | Banerjee S., Northwestern University, 2145 Sheridan Road, Evanston, 60208, IL, United States; Rodrigues M., Siemens Medical Solutions USA, Inc., Hoffman Estates, 60192, IL, United States; Ballester M., Northwestern University, 2145 Sheridan Road, Evanston, 60208, IL, United States; Vija A.H., Siemens Medical Solutions USA, Inc., Hoffman Estates, 60192, IL, United States; Katsaggelos A.K., Northwestern University, 2145 Sheridan Road, Evanston, 60208, IL, United States |
| 3rd row | Agraz M., Department of Statistics, Giresun University, Giresun, 28200, Turkey |
| 4th row | Cen J., School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China; Zou Q., School of Computer Science and Engineering, Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, 510006, China |
| 5th row | Yao L., Department of Astronautics Science and Mechanics, Harbin Institute of Technology, Harbin, China; Wang J., Department of Astronautics Science and Mechanics, Harbin Institute of Technology, Harbin, China; Chuai M., Department of Astronautics Science and Mechanics, Harbin Institute of Technology, Harbin, China; Lomov S.V., Department of Materials Engineering, KU Leuven, Belgium; Carvelli V., Department A.B.C., Politecnico di Milano, Italy |
| Value | Count | Frequency (%) |
| of | 24547 | 8.1% |
| university | 14393 | 4.7% |
| and | 10607 | 3.5% |
| engineering | 8980 | 3.0% |
| china | 7841 | 2.6% |
| united | 6420 | 2.1% |
| department | 6313 | 2.1% |
| states | 5738 | 1.9% |
| technology | 4349 | 1.4% |
| school | 4028 | 1.3% |
| Other values (16177) | 210414 |
Most occurring characters
| Value | Count | Frequency (%) |
| 299198 | 12.9% | |
| n | 174253 | 7.5% |
| e | 173922 | 7.5% |
| i | 158060 | 6.8% |
| a | 143667 | 6.2% |
| t | 118583 | 5.1% |
| o | 112525 | 4.9% |
| , | 107261 | 4.6% |
| r | 94980 | 4.1% |
| s | 66359 | 2.9% |
| Other values (133) | 869321 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 2318129 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 299198 | 12.9% | |
| n | 174253 | 7.5% |
| e | 173922 | 7.5% |
| i | 158060 | 6.8% |
| a | 143667 | 6.2% |
| t | 118583 | 5.1% |
| o | 112525 | 4.9% |
| , | 107261 | 4.6% |
| r | 94980 | 4.1% |
| s | 66359 | 2.9% |
| Other values (133) | 869321 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 2318129 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 299198 | 12.9% | |
| n | 174253 | 7.5% |
| e | 173922 | 7.5% |
| i | 158060 | 6.8% |
| a | 143667 | 6.2% |
| t | 118583 | 5.1% |
| o | 112525 | 4.9% |
| , | 107261 | 4.6% |
| r | 94980 | 4.1% |
| s | 66359 | 2.9% |
| Other values (133) | 869321 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 2318129 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 299198 | 12.9% | |
| n | 174253 | 7.5% |
| e | 173922 | 7.5% |
| i | 158060 | 6.8% |
| a | 143667 | 6.2% |
| t | 118583 | 5.1% |
| o | 112525 | 4.9% |
| , | 107261 | 4.6% |
| r | 94980 | 4.1% |
| s | 66359 | 2.9% |
| Other values (133) | 869321 |
Author Keywords
Text
Missing
| Distinct | 3387 |
|---|---|
| Distinct (%) | 99.7% |
| Missing | 1042 |
| Missing (%) | 23.5% |
| Memory size | 603.1 KiB |
Length
| Max length | 695 |
|---|---|
| Median length | 208 |
| Mean length | 111.94968 |
| Min length | 18 |
Unique
| Unique | 3377 ? |
|---|---|
| Unique (%) | 99.4% |
Sample
| 1st row | Fluid dynamics; Partial differential equations; Physics-informed neural networks; Simplified NavierStokes equations; Unsteady flow |
|---|---|
| 2nd row | Finite volume method; High-dimensional PDEs; Neural network; Second order differential operator |
| 3rd row | Composite laminates; Delamination; Fatigue; Fiber bridging; Physics-informed neural networks (PINNs) |
| 4th row | Kolmogorov n-width; Multitask learning; Neural operators; Physics-informed neural networks (PINNs) |
| 5th row | Composite laminated plates; Inverse problem; Physical-constrained neural networks; Transfer learning |
| Value | Count | Frequency (%) |
| neural | 2476 | 6.2% |
| learning | 2116 | 5.3% |
| physics-informed | 1806 | 4.5% |
| networks | 1364 | 3.4% |
| network | 1176 | 3.0% |
| machine | 1134 | 2.8% |
| deep | 854 | 2.1% |
| model | 424 | 1.1% |
| equation | 416 | 1.0% |
| physics | 346 | 0.9% |
| Other values (5375) | 27744 |
Most occurring characters
| Value | Count | Frequency (%) |
| 36454 | 9.6% | |
| e | 35219 | 9.3% |
| i | 30282 | 8.0% |
| n | 28910 | 7.6% |
| r | 24182 | 6.4% |
| a | 24004 | 6.3% |
| o | 20770 | 5.5% |
| t | 20558 | 5.4% |
| s | 18002 | 4.7% |
| l | 15721 | 4.1% |
| Other values (89) | 126303 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 380405 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 36454 | 9.6% | |
| e | 35219 | 9.3% |
| i | 30282 | 8.0% |
| n | 28910 | 7.6% |
| r | 24182 | 6.4% |
| a | 24004 | 6.3% |
| o | 20770 | 5.5% |
| t | 20558 | 5.4% |
| s | 18002 | 4.7% |
| l | 15721 | 4.1% |
| Other values (89) | 126303 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 380405 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 36454 | 9.6% | |
| e | 35219 | 9.3% |
| i | 30282 | 8.0% |
| n | 28910 | 7.6% |
| r | 24182 | 6.4% |
| a | 24004 | 6.3% |
| o | 20770 | 5.5% |
| t | 20558 | 5.4% |
| s | 18002 | 4.7% |
| l | 15721 | 4.1% |
| Other values (89) | 126303 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 380405 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 36454 | 9.6% | |
| e | 35219 | 9.3% |
| i | 30282 | 8.0% |
| n | 28910 | 7.6% |
| r | 24182 | 6.4% |
| a | 24004 | 6.3% |
| o | 20770 | 5.5% |
| t | 20558 | 5.4% |
| s | 18002 | 4.7% |
| l | 15721 | 4.1% |
| Other values (89) | 126303 |
Publisher
Text
| Distinct | 286 |
|---|---|
| Distinct (%) | 6.5% |
| Missing | 7 |
| Missing (%) | 0.2% |
| Memory size | 371.8 KiB |
Length
| Max length | 84 |
|---|---|
| Median length | 72 |
| Mean length | 28.815926 |
| Min length | 4 |
Unique
| Unique | 124 ? |
|---|---|
| Unique (%) | 2.8% |
Sample
| 1st row | Elsevier B.V. |
|---|---|
| 2nd row | Nature Research |
| 3rd row | Nature Research |
| 4th row | Academic Press Inc. |
| 5th row | Elsevier Ltd |
| Value | Count | Frequency (%) |
| and | 1296 | 7.4% |
| of | 1246 | 7.1% |
| elsevier | 1161 | 6.6% |
| inc | 986 | 5.6% |
| institute | 959 | 5.5% |
| engineers | 782 | 4.5% |
| ltd | 780 | 4.5% |
| electronics | 572 | 3.3% |
| electrical | 571 | 3.3% |
| b.v | 538 | 3.1% |
| Other values (407) | 8589 |
Most occurring characters
| Value | Count | Frequency (%) |
| 13048 | 10.2% | |
| e | 12339 | 9.7% |
| i | 10470 | 8.2% |
| n | 9868 | 7.7% |
| s | 7982 | 6.2% |
| t | 7523 | 5.9% |
| c | 7505 | 5.9% |
| r | 6573 | 5.1% |
| a | 5389 | 4.2% |
| l | 4921 | 3.9% |
| Other values (56) | 42123 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 127741 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 13048 | 10.2% | |
| e | 12339 | 9.7% |
| i | 10470 | 8.2% |
| n | 9868 | 7.7% |
| s | 7982 | 6.2% |
| t | 7523 | 5.9% |
| c | 7505 | 5.9% |
| r | 6573 | 5.1% |
| a | 5389 | 4.2% |
| l | 4921 | 3.9% |
| Other values (56) | 42123 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 127741 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 13048 | 10.2% | |
| e | 12339 | 9.7% |
| i | 10470 | 8.2% |
| n | 9868 | 7.7% |
| s | 7982 | 6.2% |
| t | 7523 | 5.9% |
| c | 7505 | 5.9% |
| r | 6573 | 5.1% |
| a | 5389 | 4.2% |
| l | 4921 | 3.9% |
| Other values (56) | 42123 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 127741 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 13048 | 10.2% | |
| e | 12339 | 9.7% |
| i | 10470 | 8.2% |
| n | 9868 | 7.7% |
| s | 7982 | 6.2% |
| t | 7523 | 5.9% |
| c | 7505 | 5.9% |
| r | 6573 | 5.1% |
| a | 5389 | 4.2% |
| l | 4921 | 3.9% |
| Other values (56) | 42123 |
Language of Original Document
Categorical
Imbalance
| Distinct | 8 |
|---|---|
| Distinct (%) | 0.2% |
| Missing | 4 |
| Missing (%) | 0.1% |
| Memory size | 277.6 KiB |
| English | |
|---|---|
| Chinese | 85 |
| Japanese | 4 |
| Korean | 4 |
| German | 4 |
| Other values (3) | 6 |
Length
| Max length | 9 |
|---|---|
| Median length | 7 |
| Mean length | 7.0009017 |
| Min length | 6 |
Unique
| Unique | 1 ? |
|---|---|
| Unique (%) | < 0.1% |
Sample
| 1st row | English |
|---|---|
| 2nd row | English |
| 3rd row | English |
| 4th row | English |
| 5th row | English |
Common Values
| Value | Count | Frequency (%) |
| English | 4333 | |
| Chinese | 85 | 1.9% |
| Japanese | 4 | 0.1% |
| Korean | 4 | 0.1% |
| German | 4 | 0.1% |
| Icelandic | 3 | 0.1% |
| Russian | 2 | < 0.1% |
| undefined | 1 | < 0.1% |
| (Missing) | 4 | 0.1% |
Length
Histogram of lengths of the category
Common Values (Plot)
| Value | Count | Frequency (%) |
| english | 4333 | |
| chinese | 85 | 1.9% |
| japanese | 4 | 0.1% |
| korean | 4 | 0.1% |
| german | 4 | 0.1% |
| icelandic | 3 | 0.1% |
| russian | 2 | < 0.1% |
| undefined | 1 | < 0.1% |
Most occurring characters
| Value | Count | Frequency (%) |
| n | 4437 | |
| s | 4426 | |
| i | 4424 | |
| h | 4418 | |
| l | 4336 | |
| E | 4333 | |
| g | 4333 | |
| e | 191 | 0.6% |
| C | 85 | 0.3% |
| a | 21 | 0.1% |
| Other values (13) | 52 | 0.2% |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 31056 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| n | 4437 | |
| s | 4426 | |
| i | 4424 | |
| h | 4418 | |
| l | 4336 | |
| E | 4333 | |
| g | 4333 | |
| e | 191 | 0.6% |
| C | 85 | 0.3% |
| a | 21 | 0.1% |
| Other values (13) | 52 | 0.2% |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 31056 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| n | 4437 | |
| s | 4426 | |
| i | 4424 | |
| h | 4418 | |
| l | 4336 | |
| E | 4333 | |
| g | 4333 | |
| e | 191 | 0.6% |
| C | 85 | 0.3% |
| a | 21 | 0.1% |
| Other values (13) | 52 | 0.2% |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 31056 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| n | 4437 | |
| s | 4426 | |
| i | 4424 | |
| h | 4418 | |
| l | 4336 | |
| E | 4333 | |
| g | 4333 | |
| e | 191 | 0.6% |
| C | 85 | 0.3% |
| a | 21 | 0.1% |
| Other values (13) | 52 | 0.2% |
Document Type
Categorical
High correlation Imbalance
| Distinct | 16 |
|---|---|
| Distinct (%) | 0.4% |
| Missing | 1 |
| Missing (%) | < 0.1% |
| Memory size | 288.8 KiB |
| Article | |
|---|---|
| Conference paper | |
| Conference review | 92 |
| Review | 87 |
| Book chapter | 62 |
| Other values (11) | 38 |
Length
| Max length | 23 |
|---|---|
| Median length | 7 |
| Mean length | 9.5733273 |
| Min length | 4 |
Unique
| Unique | 5 ? |
|---|---|
| Unique (%) | 0.1% |
Sample
| 1st row | Article |
|---|---|
| 2nd row | Article |
| 3rd row | Article |
| 4th row | Article |
| 5th row | Article |
Common Values
| Value | Count | Frequency (%) |
| Article | 3020 | |
| Conference paper | 1140 | 25.7% |
| Conference review | 92 | 2.1% |
| Review | 87 | 2.0% |
| Book chapter | 62 | 1.4% |
| Erratum | 13 | 0.3% |
| Note | 6 | 0.1% |
| Editorial | 5 | 0.1% |
| Book | 4 | 0.1% |
| Data paper | 3 | 0.1% |
| Other values (6) | 7 | 0.2% |
Length
Histogram of lengths of the category
| Value | Count | Frequency (%) |
| article | 3020 | |
| conference | 1232 | |
| paper | 1143 | 19.9% |
| review | 179 | 3.1% |
| book | 66 | 1.1% |
| chapter | 62 | 1.1% |
| erratum | 13 | 0.2% |
| note | 6 | 0.1% |
| editorial | 5 | 0.1% |
| data | 3 | 0.1% |
| Other values (12) | 14 | 0.2% |
Most occurring characters
| Value | Count | Frequency (%) |
| e | 8293 | |
| r | 5588 | |
| c | 4315 | |
| i | 3211 | 7.6% |
| t | 3116 | 7.3% |
| l | 3027 | 7.1% |
| A | 3021 | 7.1% |
| n | 2467 | 5.8% |
| p | 2348 | 5.5% |
| o | 1377 | 3.2% |
| Other values (26) | 5733 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 42496 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| e | 8293 | |
| r | 5588 | |
| c | 4315 | |
| i | 3211 | 7.6% |
| t | 3116 | 7.3% |
| l | 3027 | 7.1% |
| A | 3021 | 7.1% |
| n | 2467 | 5.8% |
| p | 2348 | 5.5% |
| o | 1377 | 3.2% |
| Other values (26) | 5733 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 42496 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| e | 8293 | |
| r | 5588 | |
| c | 4315 | |
| i | 3211 | 7.6% |
| t | 3116 | 7.3% |
| l | 3027 | 7.1% |
| A | 3021 | 7.1% |
| n | 2467 | 5.8% |
| p | 2348 | 5.5% |
| o | 1377 | 3.2% |
| Other values (26) | 5733 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 42496 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| e | 8293 | |
| r | 5588 | |
| c | 4315 | |
| i | 3211 | 7.6% |
| t | 3116 | 7.3% |
| l | 3027 | 7.1% |
| A | 3021 | 7.1% |
| n | 2467 | 5.8% |
| p | 2348 | 5.5% |
| o | 1377 | 3.2% |
| Other values (26) | 5733 |
Open Access
Categorical
High correlation Missing
| Distinct | 10 |
|---|---|
| Distinct (%) | 0.5% |
| Missing | 2528 |
| Missing (%) | 56.9% |
| Memory size | 314.0 KiB |
| All Open Access; Green Open Access | |
|---|---|
| All Open Access; Gold Open Access | |
| All Open Access; Hybrid Gold Open Access | |
| All Open Access; Bronze Open Access | |
| All Open Access; Gold Open Access; Green Open Access | |
| Other values (5) |
Length
| Max length | 59 |
|---|---|
| Median length | 54 |
| Mean length | 37.055962 |
| Min length | 4 |
Unique
| Unique | 2 ? |
|---|---|
| Unique (%) | 0.1% |
Sample
| 1st row | All Open Access; Hybrid Gold Open Access |
|---|---|
| 2nd row | All Open Access; Gold Open Access |
| 3rd row | All Open Access; Gold Open Access |
| 4th row | All Open Access; Green Open Access |
| 5th row | All Open Access; Green Open Access |
Common Values
| Value | Count | Frequency (%) |
| All Open Access; Green Open Access | 567 | 12.8% |
| All Open Access; Gold Open Access | 554 | 12.5% |
| All Open Access; Hybrid Gold Open Access | 343 | 7.7% |
| All Open Access; Bronze Open Access | 238 | 5.4% |
| All Open Access; Gold Open Access; Green Open Access | 124 | 2.8% |
| All Open Access; Green Open Access; Hybrid Gold Open Access | 69 | 1.6% |
| All Open Access; Bronze Open Access; Green Open Access | 13 | 0.3% |
| Final | 2 | < 0.1% |
| final | 1 | < 0.1% |
| Book | 1 | < 0.1% |
| (Missing) | 2528 |
Length
Histogram of lengths of the category
Common Values (Plot)
| Value | Count | Frequency (%) |
| open | 4022 | |
| access | 4022 | |
| all | 1908 | |
| gold | 1090 | 8.7% |
| green | 773 | 6.2% |
| hybrid | 412 | 3.3% |
| bronze | 251 | 2.0% |
| final | 3 | < 0.1% |
| book | 1 | < 0.1% |
Most occurring characters
| Value | Count | Frequency (%) |
| 10571 | ||
| e | 9841 | |
| c | 8044 | |
| s | 8044 | |
| A | 5930 | |
| n | 5049 | |
| l | 4909 | |
| O | 4022 | 5.7% |
| p | 4022 | 5.7% |
| ; | 2114 | 3.0% |
| Other values (14) | 8305 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 70851 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 10571 | ||
| e | 9841 | |
| c | 8044 | |
| s | 8044 | |
| A | 5930 | |
| n | 5049 | |
| l | 4909 | |
| O | 4022 | 5.7% |
| p | 4022 | 5.7% |
| ; | 2114 | 3.0% |
| Other values (14) | 8305 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 70851 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 10571 | ||
| e | 9841 | |
| c | 8044 | |
| s | 8044 | |
| A | 5930 | |
| n | 5049 | |
| l | 4909 | |
| O | 4022 | 5.7% |
| p | 4022 | 5.7% |
| ; | 2114 | 3.0% |
| Other values (14) | 8305 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 70851 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 10571 | ||
| e | 9841 | |
| c | 8044 | |
| s | 8044 | |
| A | 5930 | |
| n | 5049 | |
| l | 4909 | |
| O | 4022 | 5.7% |
| p | 4022 | 5.7% |
| ; | 2114 | 3.0% |
| Other values (14) | 8305 |
EID
Text
Unique
| Distinct | 4440 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 325.3 KiB |
Length
| Max length | 18 |
|---|---|
| Median length | 18 |
| Mean length | 18 |
| Min length | 18 |
Unique
| Unique | 4440 ? |
|---|---|
| Unique (%) | 100.0% |
Sample
| 1st row | 2-s2.0-85199357735 |
|---|---|
| 2nd row | 2-s2.0-85189357733 |
| 3rd row | 2-s2.0-85201391255 |
| 4th row | 2-s2.0-85200113536 |
| 5th row | 2-s2.0-85204075898 |
| Value | Count | Frequency (%) |
| 2-s2.0-85199357735 | 1 | < 0.1% |
| 2-s2.0-85203655512 | 1 | < 0.1% |
| 2-s2.0-85200113536 | 1 | < 0.1% |
| 2-s2.0-85204075898 | 1 | < 0.1% |
| 2-s2.0-85204053479 | 1 | < 0.1% |
| 2-s2.0-85201877813 | 1 | < 0.1% |
| 2-s2.0-85204690331 | 1 | < 0.1% |
| 2-s2.0-85200217550 | 1 | < 0.1% |
| 2-s2.0-85204772188 | 1 | < 0.1% |
| 2-s2.0-85197687438 | 1 | < 0.1% |
| Other values (4430) | 4430 |
Most occurring characters
| Value | Count | Frequency (%) |
| 2 | 12764 | |
| - | 8880 | |
| 0 | 8261 | |
| 8 | 8200 | |
| 5 | 7620 | |
| 1 | 7066 | |
| s | 4440 | 5.6% |
| . | 4440 | 5.6% |
| 9 | 3902 | 4.9% |
| 7 | 3675 | 4.6% |
| Other values (3) | 10672 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 79920 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 2 | 12764 | |
| - | 8880 | |
| 0 | 8261 | |
| 8 | 8200 | |
| 5 | 7620 | |
| 1 | 7066 | |
| s | 4440 | 5.6% |
| . | 4440 | 5.6% |
| 9 | 3902 | 4.9% |
| 7 | 3675 | 4.6% |
| Other values (3) | 10672 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 79920 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 2 | 12764 | |
| - | 8880 | |
| 0 | 8261 | |
| 8 | 8200 | |
| 5 | 7620 | |
| 1 | 7066 | |
| s | 4440 | 5.6% |
| . | 4440 | 5.6% |
| 9 | 3902 | 4.9% |
| 7 | 3675 | 4.6% |
| Other values (3) | 10672 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 79920 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 2 | 12764 | |
| - | 8880 | |
| 0 | 8261 | |
| 8 | 8200 | |
| 5 | 7620 | |
| 1 | 7066 | |
| s | 4440 | 5.6% |
| . | 4440 | 5.6% |
| 9 | 3902 | 4.9% |
| 7 | 3675 | 4.6% |
| Other values (3) | 10672 |
Interactions
Correlations
| Cited by | Document Type | Language of Original Document | Open Access | Year | |
|---|---|---|---|---|---|
| Cited by | 1.000 | 0.022 | 0.000 | 0.000 | -0.585 |
| Document Type | 0.022 | 1.000 | 0.233 | 0.503 | 0.140 |
| Language of Original Document | 0.000 | 0.233 | 1.000 | 0.026 | 0.179 |
| Open Access | 0.000 | 0.503 | 0.026 | 1.000 | 0.129 |
| Year | -0.585 | 0.140 | 0.179 | 0.129 | 1.000 |
Missing values
A simple visualization of nullity by column.
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
Sample
| Author full names | Author(s) ID | Title | Year | Source title | Cited by | Affiliations | Authors with affiliations | Author Keywords | Publisher | Language of Original Document | Document Type | Open Access | EID | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Puri, Rishabh (59230247300); Onishi, Junya (7003942439); Rüttgers, Mario (57205467321); Sarma, Rakesh (56448274800); Tsubokura, Makoto (57543345100); Lintermann, Andreas (36573883800) | 59230247300; 7003942439; 57205467321; 56448274800; 57543345100; 36573883800 | On the choice of physical constraints in artificial neural networks for predicting flow fields | 2024 | Future Generation Computer Systems | 0 | Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich, 52425, Germany; RIKEN Center for Computational Science, 7-1-26, Minatojima-minami-machi, Chuo-ku, Hyogo, Kobe, 650-0047, Japan; Engler-Bunte Institute, Combustion Technology, Karlsruhe Institute for Technology, Engler-Bunte Ring 7, Karlsruhe, 76131, Germany | Puri R., Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich, 52425, Germany, Engler-Bunte Institute, Combustion Technology, Karlsruhe Institute for Technology, Engler-Bunte Ring 7, Karlsruhe, 76131, Germany; Onishi J., RIKEN Center for Computational Science, 7-1-26, Minatojima-minami-machi, Chuo-ku, Hyogo, Kobe, 650-0047, Japan; Rüttgers M., Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich, 52425, Germany; Sarma R., Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich, 52425, Germany; Tsubokura M., RIKEN Center for Computational Science, 7-1-26, Minatojima-minami-machi, Chuo-ku, Hyogo, Kobe, 650-0047, Japan; Lintermann A., Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich, 52425, Germany | Fluid dynamics; Partial differential equations; Physics-informed neural networks; Simplified NavierStokes equations; Unsteady flow | Elsevier B.V. | English | Article | All Open Access; Hybrid Gold Open Access | 2-s2.0-85199357735 |
| 1 | Banerjee, Srutarshi (57212204932); Rodrigues, Miesher (24483782300); Ballester, Manuel (57336856100); Vija, Alexander H. (8521136100); Katsaggelos, Aggelos K. (7102711302) | 57212204932; 24483782300; 57336856100; 8521136100; 7102711302 | A physics based machine learning model to characterize room temperature semiconductor detectors in 3D | 2024 | Scientific Reports | 0 | Northwestern University, 2145 Sheridan Road, Evanston, 60208, IL, United States; Siemens Medical Solutions USA, Inc., Hoffman Estates, 60192, IL, United States | Banerjee S., Northwestern University, 2145 Sheridan Road, Evanston, 60208, IL, United States; Rodrigues M., Siemens Medical Solutions USA, Inc., Hoffman Estates, 60192, IL, United States; Ballester M., Northwestern University, 2145 Sheridan Road, Evanston, 60208, IL, United States; Vija A.H., Siemens Medical Solutions USA, Inc., Hoffman Estates, 60192, IL, United States; Katsaggelos A.K., Northwestern University, 2145 Sheridan Road, Evanston, 60208, IL, United States | NaN | Nature Research | English | Article | All Open Access; Gold Open Access | 2-s2.0-85189357733 |
| 2 | Agraz, Melih (57188574966) | 57188574966 | Evaluating single multiplicative neuron models in physics-informed neural networks for differential equations | 2024 | Scientific Reports | 0 | Department of Statistics, Giresun University, Giresun, 28200, Turkey | Agraz M., Department of Statistics, Giresun University, Giresun, 28200, Turkey | NaN | Nature Research | English | Article | All Open Access; Gold Open Access | 2-s2.0-85201391255 |
| 3 | Cen, Jianhuan (58172058200); Zou, Qingsong (8369763900) | 58172058200; 8369763900 | Deep finite volume method for partial differential equations | 2024 | Journal of Computational Physics | 0 | School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China; School of Computer Science and Engineering, Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, 510006, China | Cen J., School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China; Zou Q., School of Computer Science and Engineering, Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, 510006, China | Finite volume method; High-dimensional PDEs; Neural network; Second order differential operator | Academic Press Inc. | English | Article | All Open Access; Green Open Access | 2-s2.0-85200113536 |
| 4 | Yao, Liaojun (54584768400); Wang, Jiexiong (57776191800); Chuai, Mingyue (58485210500); Lomov, Stepan V. (7005067917); Carvelli, V. (6603539304) | 54584768400; 57776191800; 58485210500; 7005067917; 6603539304 | Physics-informed machine learning for loading history dependent fatigue delamination of composite laminates | 2024 | Composites Part A: Applied Science and Manufacturing | 0 | Department of Astronautics Science and Mechanics, Harbin Institute of Technology, Harbin, China; Department of Materials Engineering, KU Leuven, Belgium; Department A.B.C., Politecnico di Milano, Italy | Yao L., Department of Astronautics Science and Mechanics, Harbin Institute of Technology, Harbin, China; Wang J., Department of Astronautics Science and Mechanics, Harbin Institute of Technology, Harbin, China; Chuai M., Department of Astronautics Science and Mechanics, Harbin Institute of Technology, Harbin, China; Lomov S.V., Department of Materials Engineering, KU Leuven, Belgium; Carvelli V., Department A.B.C., Politecnico di Milano, Italy | Composite laminates; Delamination; Fatigue; Fiber bridging; Physics-informed neural networks (PINNs) | Elsevier Ltd | English | Article | NaN | 2-s2.0-85204075898 |
| 5 | Penwarden, Michael (57225220864); Owhadi, Houman (6602245780); Kirby, Robert M. (7201552592) | 57225220864; 6602245780; 7201552592 | Kolmogorov n-widths for multitask physics-informed machine learning (PIML) methods: Towards robust metrics | 2024 | Neural Networks | 0 | Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, 84112, UT, United States; Kahlert School of Computing, University of Utah, Salt Lake City, 84112, UT, United States; Department of Computing and Mathematical Sciences, Caltech, Pasadena, 91125, CA, United States | Penwarden M., Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, 84112, UT, United States, Kahlert School of Computing, University of Utah, Salt Lake City, 84112, UT, United States; Owhadi H., Department of Computing and Mathematical Sciences, Caltech, Pasadena, 91125, CA, United States; Kirby R.M., Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, 84112, UT, United States, Kahlert School of Computing, University of Utah, Salt Lake City, 84112, UT, United States | Kolmogorov n-width; Multitask learning; Neural operators; Physics-informed neural networks (PINNs) | Elsevier Ltd | English | Article | All Open Access; Green Open Access | 2-s2.0-85204053479 |
| 6 | Li, Yang (59012879300); Wan, Detao (56999850900); Wang, Zhe (59032214000); Hu, Dean (36602497000) | 59012879300; 56999850900; 59032214000; 36602497000 | Physics-constrained deep learning approach for solving inverse problems in composite laminated plates | 2024 | Composite Structures | 0 | Key Laboratory of Advanced Design and Simulation Techniques for Special Equipment, Ministry of Education, Hunan University, Changsha, 410082, China | Li Y., Key Laboratory of Advanced Design and Simulation Techniques for Special Equipment, Ministry of Education, Hunan University, Changsha, 410082, China; Wan D., Key Laboratory of Advanced Design and Simulation Techniques for Special Equipment, Ministry of Education, Hunan University, Changsha, 410082, China; Wang Z., Key Laboratory of Advanced Design and Simulation Techniques for Special Equipment, Ministry of Education, Hunan University, Changsha, 410082, China; Hu D., Key Laboratory of Advanced Design and Simulation Techniques for Special Equipment, Ministry of Education, Hunan University, Changsha, 410082, China | Composite laminated plates; Inverse problem; Physical-constrained neural networks; Transfer learning | Elsevier Ltd | English | Article | NaN | 2-s2.0-85201877813 |
| 7 | Sen, Ahmet (59110359400); Ghajar-Rahimi, Elnaz (57218339760); Aguirre, Miquel (55977418200); Navarro, Laurent (57211805601); Goergen, Craig J. (22134531900); Avril, Stephane (6602427519) | 59110359400; 57218339760; 55977418200; 57211805601; 22134531900; 6602427519 | Physics-Informed Graph Neural Networks to solve 1-D equations of blood flow | 2024 | Computer Methods and Programs in Biomedicine | 0 | Mines Saint-Etienne, Univ Jean Monnet, INSERM, U 1059, Sainbiose, F-42023, France; Weldon School of Biomedical Engineering, Purdue University, West Lafayette, 47907, IN, United States; CIMNE, Gran Capità, 08034, Spain; LaCàN, Universitat Politècnica de Catalunya, Jordi Girona 1, Barcelona, E-08034, Spain | Sen A., Mines Saint-Etienne, Univ Jean Monnet, INSERM, U 1059, Sainbiose, F-42023, France; Ghajar-Rahimi E., Weldon School of Biomedical Engineering, Purdue University, West Lafayette, 47907, IN, United States; Aguirre M., CIMNE, Gran Capità, 08034, Spain, LaCàN, Universitat Politècnica de Catalunya, Jordi Girona 1, Barcelona, E-08034, Spain; Navarro L., Mines Saint-Etienne, Univ Jean Monnet, INSERM, U 1059, Sainbiose, F-42023, France; Goergen C.J., Weldon School of Biomedical Engineering, Purdue University, West Lafayette, 47907, IN, United States; Avril S., Mines Saint-Etienne, Univ Jean Monnet, INSERM, U 1059, Sainbiose, F-42023, France | Blood flow modeling; Graph neural network; Machine learning; Physics-Informed Neural Networks; Pulse wave propagation | Elsevier Ireland Ltd | English | Article | All Open Access; Hybrid Gold Open Access | 2-s2.0-85204690331 |
| 8 | Hedayatrasa, Saeid (55845267000); Fink, Olga (54961523800); Van Paepegem, Wim (9640465200); Kersemans, Mathias (54958029400) | 55845267000; 54961523800; 9640465200; 54958029400 | k-space physics-informed neural network (k-PINN) for compressed spectral mapping and efficient inversion of vibrations in thin composite laminates | 2025 | Mechanical Systems and Signal Processing | 0 | Mechanics of Materials and Structures (UGent-MMS), Department of Materials, Textiles and Chemical Engineering (MaTCh), Ghent University, Technologiepark-Zwijnaarde 46, Zwijnaarde, 9052, Belgium; Flanders Make-MotionS, Lommel, 3920, Belgium; Intelligent Maintenance and Operating Systems (IMOS) lab, EPFL, Switzerland | Hedayatrasa S., Mechanics of Materials and Structures (UGent-MMS), Department of Materials, Textiles and Chemical Engineering (MaTCh), Ghent University, Technologiepark-Zwijnaarde 46, Zwijnaarde, 9052, Belgium, Flanders Make-MotionS, Lommel, 3920, Belgium; Fink O., Intelligent Maintenance and Operating Systems (IMOS) lab, EPFL, Switzerland; Van Paepegem W., Mechanics of Materials and Structures (UGent-MMS), Department of Materials, Textiles and Chemical Engineering (MaTCh), Ghent University, Technologiepark-Zwijnaarde 46, Zwijnaarde, 9052, Belgium; Kersemans M., Mechanics of Materials and Structures (UGent-MMS), Department of Materials, Textiles and Chemical Engineering (MaTCh), Ghent University, Technologiepark-Zwijnaarde 46, Zwijnaarde, 9052, Belgium | Composite; Elastic Coefficients; Inversion; K-space; Physics-informed Neural Networks; Spectral bias; Vibration | Academic Press | English | Article | NaN | 2-s2.0-85203655512 |
| 9 | Duong, Tien Trung (57205516680); Jung, Kwang Hyo (7402479754); Lee, Gang Nam (57191165522); Suh, Sung Bu (35791359900) | 57205516680; 7402479754; 57191165522; 35791359900 | Physics-informed neural network for the reconstruction of velocity and pressure of wave-in-deck loading from particle image velocimetry data | 2024 | Applied Ocean Research | 0 | Department of Naval Architecture and Ocean Engineering, Pusan National University, 2, Busandaehak-ro, Busan, 46241, South Korea; Department of Ocean System Engineering, Jeju National University, 102 Jejudaehak-ro, Jeju, Jeju-si, 63243, South Korea; Department of Naval Architecture and Ocean Engineering, Dong-Eui University, 176, Eomgwang-ro, Busan, 47340, South Korea | Duong T.T., Department of Naval Architecture and Ocean Engineering, Pusan National University, 2, Busandaehak-ro, Busan, 46241, South Korea; Jung K.H., Department of Naval Architecture and Ocean Engineering, Pusan National University, 2, Busandaehak-ro, Busan, 46241, South Korea; Lee G.N., Department of Ocean System Engineering, Jeju National University, 102 Jejudaehak-ro, Jeju, Jeju-si, 63243, South Korea; Suh S.B., Department of Naval Architecture and Ocean Engineering, Dong-Eui University, 176, Eomgwang-ro, Busan, 47340, South Korea | Euler equation; Particle image velocimetry; Physics-informed neural network; Pressure fields; Velocity profile; Wave-in-deck load | Elsevier Ltd | English | Article | NaN | 2-s2.0-85202944549 |
| Author full names | Author(s) ID | Title | Year | Source title | Cited by | Affiliations | Authors with affiliations | Author Keywords | Publisher | Language of Original Document | Document Type | Open Access | EID | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4430 | Butler, Philip (58180559800) | 58180559800 | Humanism and the Conceptualization of Value and Well-Being | 2019 | The oxford Handbook of Humanism | 0 | Theology and Black Posthuman Artificial Intelligence Systems, Iliff School of Theology, United States | Butler P., Theology and Black Posthuman Artificial Intelligence Systems, Iliff School of Theology, United States | African American humanism; decolonial studies; epistemology; humanism; philosophy; spirituality; transcendence; well-being | Oxford University Press | English | Book chapter | NaN | 2-s2.0-85133092793 |
| 4431 | Zhang, Dongkun (57200302259); Lu, Lu (57189226888); Guo, Ling (44461317100); Karniadakis, George Em (55665002100) | 57200302259; 57189226888; 44461317100; 55665002100 | Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems | 2019 | Journal of Computational Physics | 271 | Division of Applied Mathematics, Brown University, Providence, RI, United States; Department of Mathematics, Shanghai Normal University, Shanghai, China | Zhang D., Division of Applied Mathematics, Brown University, Providence, RI, United States; Lu L., Division of Applied Mathematics, Brown University, Providence, RI, United States; Guo L., Department of Mathematics, Shanghai Normal University, Shanghai, China; Karniadakis G.E., Division of Applied Mathematics, Brown University, Providence, RI, United States | Arbitrary polynomial chaos; Dropout; Physics-informed neural networks; Stochastic differential equations; Uncertainty quantification | Academic Press Inc. | English | Article | All Open Access; Bronze Open Access | 2-s2.0-85070222554 |
| 4432 | Simmons, Cody R. (57215008255); Arment, Joshua R. (57214998078); Powell, Kody M. (51461952600); Hedengren, John D. (9277159100) | 57215008255; 57214998078; 51461952600; 9277159100 | Proactive energy optimization in residential buildings withweather and market forecasts | 2019 | Processes | 14 | Department of Chemical Engineering, Brigham Young University, Provo, 84602, UT, United States; Department of Chemical Engineering, University of Utah, Salt Lake City, 84112, UT, United States | Simmons C.R., Department of Chemical Engineering, Brigham Young University, Provo, 84602, UT, United States; Arment J.R., Department of Chemical Engineering, Brigham Young University, Provo, 84602, UT, United States; Powell K.M., Department of Chemical Engineering, University of Utah, Salt Lake City, 84112, UT, United States; Hedengren J.D., Department of Chemical Engineering, Brigham Young University, Provo, 84602, UT, United States | Dynamic optimization; Energy storage; Forecast; HEMS; Home energy optimization; Model predictive control; Moving horizon estimation; Solar generation; Thermal modeling | MDPI AG | English | Article | All Open Access; Gold Open Access; Green Open Access | 2-s2.0-85079639165 |
| 4433 | Werhahn, Maximilian (57219619923); Xie, You (57204697503); Chu, Mengyu (57196000654); Thuerey, Nils (26024848800) | 57219619923; 57204697503; 57196000654; 26024848800 | A multi-pass GaN for fluid flow super-resolution | 2019 | Proceedings of the ACM on Computer Graphics and Interactive Techniques | 42 | Technical University of Munich, Germany | Werhahn M., Technical University of Munich, Germany; Xie Y., Technical University of Munich, Germany; Chu M., Technical University of Munich, Germany; Thuerey N., Technical University of Munich, Germany | Computer animation; Fluid simulation; Generative models; Physics-based deep learning | Association for Computing Machinery | English | Article | All Open Access; Green Open Access | 2-s2.0-85092777667 |
| 4434 | Pinn, Anthony B. (37096219900); Driscoll, Christopher M. (57091215500) | 37096219900; 57091215500 | Introduction: K.dotting the american cultural landscape with black meaning | 2019 | Kendrick Lamar and the Making of Black Meaning | 0 | Rice University, United States; Lehigh University, United States | Pinn A.B., Rice University, United States; Driscoll C.M., Lehigh University, United States | NaN | Taylor and Francis | English | Book chapter | NaN | 2-s2.0-85084877159 |
| 4435 | Guan, Haowen (57188932152); Li, Qingzhong (56413173800); Yan, Zhongmin (55286550700); Wei, Wei (57020492500) | 57188932152; 56413173800; 55286550700; 57020492500 | SLOF: Identify density-based local outliers in big data | 2016 | Proceedings - 2015 12th Web Information System and Application Conference, WISA 2015 | 11 | School of Computer Science and Technology, Shandong University, Jinan, China; Shandong Hoteam Software Co., Ltd., Jinan, China | Guan H., School of Computer Science and Technology, Shandong University, Jinan, China; Li Q., School of Computer Science and Technology, Shandong University, Jinan, China; Yan Z., School of Computer Science and Technology, Shandong University, Jinan, China; Wei W., Shandong Hoteam Software Co., Ltd., Jinan, China | Data mining; Density-based outlier detection; Feature bagging; LOF; PINN; SLOF | Institute of Electrical and Electronics Engineers Inc. | English | Conference paper | NaN | 2-s2.0-84964306898 |
| 4436 | Chang, Chih-Wei (57142655400); Dinh, Nam (57208696068) | 57142655400; 57208696068 | A study of physics-informed deep learning for system fluid dynamics closures | 2016 | Transactions of the American Nuclear Society | 6 | North Carolina State University, Raleigh, 27695-7909, NC, United States | Chang C.-W., North Carolina State University, Raleigh, 27695-7909, NC, United States; Dinh N., North Carolina State University, Raleigh, 27695-7909, NC, United States | NaN | American Nuclear Society | English | Conference paper | NaN | 2-s2.0-85033222005 |
| 4437 | Liu, Lu (56176397700); Wang, Dan (57842016600); Peng, Zhouhua (35230939100) | 56176397700; 57842016600; 35230939100 | Path following of marine surface vehicles with dynamical uncertainty and time-varying ocean disturbances | 2016 | Neurocomputing | 95 | School of Marine Engineering, Dalian Maritime University, Dalian, 116026, China | Liu L., School of Marine Engineering, Dalian Maritime University, Dalian, 116026, China; Wang D., School of Marine Engineering, Dalian Maritime University, Dalian, 116026, China; Peng Z., School of Marine Engineering, Dalian Maritime University, Dalian, 116026, China | Iterative updating law; Line-of-sight; Marine surface vehicles; Neural network; Path following; Predictor | Elsevier B.V. | English | Article | NaN | 2-s2.0-84959326260 |
| 4438 | Már, Hjalti (57193699116) | 57193699116 | Pekkir pú pinn rétt? Um FOSL | 2016 | Laeknabladid | 0 | FOSL, Iceland | Már H., FOSL, Iceland | NaN | Laeknafelag Islands | Icelandic | Note | NaN | 2-s2.0-84978208543 |
| 4439 | Porkelsson, Eyjólfur (36715292300) | 36715292300 | "Ég trúi pví, sannleiki, ao sigurinn pinn ao síoustu vegina jafni" | 2016 | Laeknabladid | 0 | Skáni, Iceland | Porkelsson E., Skáni, Iceland | NaN | Laeknafelag Islands | Icelandic | Note | NaN | 2-s2.0-84978245375 |